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| Section | Weight | Objectives |
|---|---|---|
| OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
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問題 #25
Which statement describes an LLM-based AI agent?
答案：B
解題說明：
An LLM-based AI agent is not simply a foundation model or chatbot user interface. It is a system in which an LLM acts as a reasoning and decision-making component while an orchestration layer gives it access to instructions, tools, state, external information, and potentially other agents. This architecture enables the system to decide which actions to perform and in what sequence to pursue a defined objective.
OpenAI describes an agent as an LLM equipped with instructions, tools, and handoffs, allowing it to plan, use tools to gather information or take actions, and delegate tasks when appropriate. Oracle similarly explains that AI agents use tools to communicate with external systems and dynamically determine which tools or integrations to use and in which order to achieve a goal.
An agent therefore does not require training an entirely new model architecture. The underlying LLM may be an existing pretrained model. What makes the system agentic is the combination of model reasoning with orchestration, tool execution, observations, state, and iterative decision-making.
Accordingly, D provides the correct architectural definition and matches the answer supplied in the uploaded source.
Study Guide reference/topic: Introduction to AI Agents - LLM-based agents, reasoning, tools, orchestration, actions, observations, and agent loops.
問題 #26
What integration problem does MCP address?
答案：B
解題說明：
MCP addresses the integration fragmentation created when multiple AI applications must independently connect to multiple external tools, services, and data sources. The uploaded course material characterizes this explicitly as the N × M custom-connector problem : without a common interoperability layer, every application-to-tool pairing can require a separate integration.
The official MCP architecture supports this framing by defining a standardized client-server protocol. MCP hosts establish clients that communicate with MCP servers, while servers expose reusable capabilities such as tools, resources, and prompts. A compatible AI application therefore consumes capabilities through the MCP protocol rather than requiring a completely bespoke protocol implementation for each downstream system.
MCP's tool-discovery mechanism further allows clients to obtain standardized names, descriptions, and schemas dynamically.
The problem is architectural interoperability, not GPU allocation, inference latency, or context-window limitations. Those issues require separate model, infrastructure, or prompt-management techniques. MCP instead standardizes the boundary between AI applications and external capabilities, reducing duplicated connector logic and enabling reusable integrations.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - interoperability, MCP client
/server architecture, tool discovery, and the N × M integration problem.
問題 #27
How do On-Demand serving and Dedicated AI Clusters differ for OCI Enterprise AI Models?
答案：C
解題說明：
OCI Generative AI provides both On-Demand and Dedicated serving modes. The fundamental architectural distinction is resource isolation. On-Demand model inference uses Oracle-managed shared infrastructure, making it suitable for straightforward consumption of supported pretrained models without reserving dedicated hardware. Dedicated mode assigns dedicated GPU resources to the customer's workload and provides predictable, isolated serving capacity.
Oracle states that Dedicated AI Clusters are compute resources dedicated to a customer's models and are not shared with other tenancies. They are used for hosting supported pretrained models and, critically, for fine- tuning and hosting custom or imported models. The course question correspondingly identifies B as correct.
Option A ignores the infrastructure-isolation difference. Option C is technically impossible because all model inference requires compute resources; On-Demand simply abstracts shared infrastructure. Option D is also incorrect because On-Demand is not limited to embedding models-OCI provides multiple supported model categories through managed inference.
Therefore, B accurately captures both shared-versus-dedicated serving and the importance of Dedicated AI Clusters for custom-model workloads.
Study Guide reference/topic: OCI Enterprise AI Agents - Enterprise AI Models, On-Demand serving, Dedicated AI Clusters, custom models, and GPU isolation.
問題 #28
Which statement describes the purpose of the OpenAI Responses API?
答案：D
解題說明：
The OpenAI Responses API is an inference and agent-interaction interface. At its fundamental level, an application supplies input together with a selected model and optional instructions, tools, or other configuration; the model then produces a response containing generated output. The uploaded course material states this core purpose directly and identifies C as correct.
OpenAI's current API reference defines the Responses endpoint as creating a model response from text, image, or file inputs and returning generated text, structured JSON, tool calls, or other supported response items. The input field provides content to the model, while the response object's output array contains items generated by the model.
Although modern Responses API functionality extends beyond simple text generation-for example, built-in tools, function calling, conversation state, structured outputs, and agentic workflows-the basic abstraction remains model input followed by generated model output.
It is not a prompt-compression billing service, a local model-hosting environment, or a foundation-model training API. Those alternatives describe completely different system responsibilities.
Therefore, C accurately expresses the core purpose being tested.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Responses endpoint, model input, generated output, tools, and agentic workflows.
問題 #29
Which OCI services are used for observability and auditing of deployed AI agents?
答案：B
解題說明：
OCI production AI architectures use the standard OCI observability and governance services to provide operational visibility and accountability. OCI Logging collects and centralizes service and application logs; OCI Generative AI hosted applications can expose deployment logs that open directly in OCI Logging and the Observability and Management service. OCI Monitoring supplies metrics and alarms for monitoring resource health and operational conditions. OCI Audit records calls made to supported OCI public API endpoints, providing an authoritative record of administrative and resource-management actions for investigation and compliance. Oracle's architecture guidance specifically recommends enabling OCI Logging, OCI Monitoring, and OCI Audit logs for critical AI-platform components. The services in the other options have legitimate OCI purposes, but they do not collectively represent the principal observability-and-auditing stack. Therefore, option A is the verified combination. Oracle Docs
問題 #30
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